{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,26]],"date-time":"2025-03-26T23:16:49Z","timestamp":1743031009811,"version":"3.40.3"},"publisher-location":"Cham","reference-count":25,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783030299996"},{"type":"electronic","value":"9783030300005"}],"license":[{"start":{"date-parts":[[2019,1,1]],"date-time":"2019-01-01T00:00:00Z","timestamp":1546300800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2019,1,1]],"date-time":"2019-01-01T00:00:00Z","timestamp":1546300800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2019]]},"DOI":"10.1007\/978-3-030-30000-5_42","type":"book-chapter","created":{"date-parts":[[2019,8,23]],"date-time":"2019-08-23T19:02:24Z","timestamp":1566586944000},"page":"333-340","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["Bringing Advanced Analytics to Manufacturing: A Systematic Mapping"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-3134-1900","authenticated-orcid":false,"given":"Hergen","family":"Wolf","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7473-3328","authenticated-orcid":false,"given":"Rafael","family":"Lorenz","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2021-2743","authenticated-orcid":false,"given":"Mathias","family":"Kraus","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7856-8729","authenticated-orcid":false,"given":"Stefan","family":"Feuerriegel","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7382-1051","authenticated-orcid":false,"given":"Torbj\u00f8rn H.","family":"Netland","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2019,8,24]]},"reference":[{"key":"42_CR1","volume-title":"Pattern Recognition and Machine Learning","author":"CM Bishop","year":"2006","unstructured":"Bishop, C.M.: Pattern Recognition and Machine Learning. Springer, New York (2006)"},{"issue":"3","key":"42_CR2","doi-asserted-by":"publisher","first-page":"1140","DOI":"10.1016\/j.ejor.2006.12.004","volume":"184","author":"R Carbonneau","year":"2008","unstructured":"Carbonneau, R., Laframboise, K., Vahidov, R.: Application of machine learning techniques for supply chain demand forecasting. Eur. J. Oper. Res. 184(3), 1140\u20131154 (2008). https:\/\/doi.org\/10.1016\/j.ejor.2006.12.004","journal-title":"Eur. J. Oper. Res."},{"key":"42_CR3","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.jii.2017.08.001","volume":"9","author":"Y Cheng","year":"2018","unstructured":"Cheng, Y., Chen, K., Sun, H., Zhang, Y., Tao, F.: Data and knowledge mining with big data towards smart production. J. Ind. Inf. Integr. 9, 1\u201313 (2018). https:\/\/doi.org\/10.1016\/j.jii.2017.08.001","journal-title":"J. Ind. Inf. Integr."},{"issue":"5","key":"42_CR4","doi-asserted-by":"publisher","first-page":"501","DOI":"10.1007\/s10845-008-0145-x","volume":"20","author":"AK Choudhary","year":"2009","unstructured":"Choudhary, A.K., Harding, J.A., Tiwari, M.K.: Data mining in manufacturing: A review based on the kind of knowledge. J. Intell. Manuf. 20(5), 501\u2013521 (2009). https:\/\/doi.org\/10.1007\/s10845-008-0145-x","journal-title":"J. Intell. Manuf."},{"key":"42_CR5","unstructured":"Chui, M., et al.: Notes from the AI frontier: Insights from hundreds of use cases. McKinsey Global Institute (2018)"},{"key":"42_CR6","volume-title":"Competing on Analytics: The New Science of Winning","author":"TH Davenport","year":"2007","unstructured":"Davenport, T.H., Harris, J.G.: Competing on Analytics: The New Science of Winning. Harvard Business School Press, Boston (2007)"},{"key":"42_CR7","series-title":"IFIP Advances in Information and Communication Technology","doi-asserted-by":"publisher","first-page":"296","DOI":"10.1007\/978-3-319-99707-0_37","volume-title":"Advances in Production Management Systems. Smart Manufacturing for Industry 4.0","author":"P-A Dreyfus","year":"2018","unstructured":"Dreyfus, P.-A., Kyritsis, D.: A framework based on predictive maintenance, zero-defect manufacturing and scheduling under uncertainty tools, to optimize production capacities of high-end quality products. In: Moon, I., Lee, G.M., Park, J., Kiritsis, D., von Cieminski, G. (eds.) APMS 2018. IAICT, vol. 536, pp. 296\u2013303. Springer, Cham (2018). https:\/\/doi.org\/10.1007\/978-3-319-99707-0_37"},{"key":"42_CR8","doi-asserted-by":"publisher","first-page":"16","DOI":"10.1016\/j.compind.2017.09.003","volume":"94","author":"CM Flath","year":"2018","unstructured":"Flath, C.M., Stein, N.: Towards a data science toolbox for industrial analytics applications. Comput. Ind. 94, 16\u201325 (2018). https:\/\/doi.org\/10.1016\/j.compind.2017.09.003","journal-title":"Comput. Ind."},{"issue":"4","key":"42_CR9","doi-asserted-by":"publisher","first-page":"969","DOI":"10.1115\/1.2194554","volume":"128","author":"J. A. Harding","year":"2006","unstructured":"Harding, J.A., Shahbaz, M., Srinivas, Kusiak, A.: Data mining in manufacturing: a review. J. Manuf. Sci. Eng. 128(4) (2006). https:\/\/doi.org\/10.1115\/1.2194554","journal-title":"Journal of Manufacturing Science and Engineering"},{"key":"42_CR10","unstructured":"Henke, N., et al.: The age of analytics: Competing in a data-driven world. McKinsey Global Institute (2016)"},{"key":"42_CR11","doi-asserted-by":"publisher","DOI":"10.1007\/978-1-4419-9011-2","volume-title":"Machine Learning","author":"T Jebara","year":"2004","unstructured":"Jebara, T.: Machine Learning. Springer, Boston (2004). https:\/\/doi.org\/10.1007\/978-1-4419-9011-2"},{"issue":"10","key":"42_CR12","doi-asserted-by":"publisher","first-page":"13448","DOI":"10.1016\/j.eswa.2011.04.063","volume":"38","author":"G K\u00f6ksal","year":"2011","unstructured":"K\u00f6ksal, G., Batmaz, \u0130., Testik, M.C.: A review of data mining applications for quality improvement in manufacturing industry. Expert Syst. Appl. 38(10), 13448\u201313467 (2011). https:\/\/doi.org\/10.1016\/j.eswa.2011.04.063","journal-title":"Expert Syst. Appl."},{"key":"42_CR13","unstructured":"Kraus, M., Feuerriegel, S., Oztekin, A.: Deep learning in business analytics and operations research: models, applications and managerial implications. https:\/\/arxiv.org\/pdf\/1806.10897.pdf"},{"key":"42_CR14","doi-asserted-by":"publisher","first-page":"54","DOI":"10.1016\/j.compind.2017.12.005","volume":"95","author":"D Lechevalier","year":"2018","unstructured":"Lechevalier, D., Narayanan, A., Rachuri, S., Foufou, S.: A methodology for the semi-automatic generation of analytical models in manufacturing. Comput. Ind. 95, 54\u201367 (2018). https:\/\/doi.org\/10.1016\/j.compind.2017.12.005","journal-title":"Comput. Ind."},{"key":"42_CR15","unstructured":"Leurent, H., de Boer, E.: The next economic growth engine: Scaling fourth industrial revolution technologies in production. World Economic Forum (2018)"},{"key":"42_CR16","unstructured":"Manyika, J., et al.: Big data: The next frontier for innovation, competition, and productivity. McKinsey Global Institute (2011)"},{"key":"42_CR17","doi-asserted-by":"crossref","unstructured":"Petersen, K., Feldt, R., Mujtaba, S., Mattsson, M.: Systematic mapping studies in software engineering. In: 12th International Conference on Evaluation and Assessment in Software Engineering, vol. 8, pp. 68\u201377 (2008)","DOI":"10.14236\/ewic\/EASE2008.8"},{"issue":"5","key":"42_CR18","doi-asserted-by":"publisher","first-page":"395","DOI":"10.1243\/095440505X32274","volume":"219","author":"DT Pham","year":"2005","unstructured":"Pham, D.T., Afify, A.A.: Machine-learning techniques and their applications in manufacturing. Proc. Inst. Mech. Eng. Part B: J. Eng. Manuf. 219(5), 395\u2013412 (2005). https:\/\/doi.org\/10.1243\/095440505X32274","journal-title":"Proc. Inst. Mech. Eng. Part B: J. Eng. Manuf."},{"key":"42_CR19","doi-asserted-by":"publisher","first-page":"170","DOI":"10.1016\/j.jmsy.2018.02.004","volume":"48","author":"M Sharp","year":"2018","unstructured":"Sharp, M., Ak, R., Hedberg, T.: A survey of the advancing use and development of machine learning in smart manufacturing. J. Manuf. Syst. 48, 170\u2013179 (2018). https:\/\/doi.org\/10.1016\/j.jmsy.2018.02.004","journal-title":"J. Manuf. Syst."},{"issue":"5","key":"42_CR20","doi-asserted-by":"publisher","first-page":"1803","DOI":"10.1016\/j.ymssp.2010.11.018","volume":"25","author":"JZ Sikorska","year":"2011","unstructured":"Sikorska, J.Z., Hodkiewicz, M., Ma, L.: Prognostic modelling options for remaining useful life estimation by industry. Mech. Syst. Signal Process. 25(5), 1803\u20131836 (2011). https:\/\/doi.org\/10.1016\/j.ymssp.2010.11.018","journal-title":"Mech. Syst. Signal Process."},{"key":"42_CR21","doi-asserted-by":"publisher","first-page":"157","DOI":"10.1016\/j.jmsy.2018.01.006","volume":"48","author":"F Tao","year":"2018","unstructured":"Tao, F., Qi, Q., Liu, A., Kusiak, A.: Data-driven smart manufacturing. J. Manuf. Syst. 48, 157\u2013169 (2018). https:\/\/doi.org\/10.1016\/j.jmsy.2018.01.006","journal-title":"J. Manuf. Syst."},{"issue":"1","key":"42_CR22","doi-asserted-by":"publisher","first-page":"4","DOI":"10.20965\/ijat.2017.p0004","volume":"11","author":"KD Thoben","year":"2017","unstructured":"Thoben, K.D., Wiesner, S., Wuest, T.: Industrie 4.0 and smart manufacturing: A review of research issues and application examples. Int. J. Autom. Technol. 11(1), 4\u201316 (2017). https:\/\/doi.org\/10.20965\/ijat.2017.p0004","journal-title":"Int. J. Autom. Technol."},{"key":"42_CR23","doi-asserted-by":"publisher","first-page":"153","DOI":"10.1016\/j.procir.2018.03.215","volume":"72","author":"AG Villanueva Zacarias","year":"2018","unstructured":"Villanueva Zacarias, A.G., Reimann, P., Mitschang, B.: A framework to guide the selection and configuration of machine-learning-based data analytics solutions in manufacturing. Procedia CIRP 72, 153\u2013158 (2018). https:\/\/doi.org\/10.1016\/j.procir.2018.03.215","journal-title":"Procedia CIRP"},{"key":"42_CR24","doi-asserted-by":"publisher","first-page":"144","DOI":"10.1016\/j.jmsy.2018.01.003","volume":"48","author":"J Wang","year":"2018","unstructured":"Wang, J., Ma, Y., Zhang, L., Gao, R.X., Wu, D.: Deep learning for smart manufacturing: Methods and applications. J. Manuf. Syst. 48, 144\u2013156 (2018). https:\/\/doi.org\/10.1016\/j.jmsy.2018.01.003","journal-title":"J. Manuf. Syst."},{"issue":"1","key":"42_CR25","doi-asserted-by":"publisher","first-page":"23","DOI":"10.1080\/21693277.2016.1192517","volume":"4","author":"T Wuest","year":"2016","unstructured":"Wuest, T., Weimer, D., Irgens, C., Thoben, K.D.: Machine learning in manufacturing: advantages, challenges, and applications. Prod. Manuf. Res. 4(1), 23\u201345 (2016). https:\/\/doi.org\/10.1080\/21693277.2016.1192517","journal-title":"Prod. Manuf. Res."}],"container-title":["IFIP Advances in Information and Communication Technology","Advances in Production Management Systems. Production Management for the Factory of the Future"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-30000-5_42","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,3,7]],"date-time":"2024-03-07T17:41:24Z","timestamp":1709833284000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-30000-5_42"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019]]},"ISBN":["9783030299996","9783030300005"],"references-count":25,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-30000-5_42","relation":{},"ISSN":["1868-4238","1868-422X"],"issn-type":[{"type":"print","value":"1868-4238"},{"type":"electronic","value":"1868-422X"}],"subject":[],"published":{"date-parts":[[2019]]},"assertion":[{"value":"24 August 2019","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"APMS","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"IFIP International Conference on Advances in Production Management Systems","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Austin, TX","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"USA","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2019","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"1 September 2019","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"5 September 2019","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"apms2019a","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/www.apms-conference.org\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"This content has been made available to all.","name":"free","label":"Free to read"}]}}